Clinical validation of AI assisted animal ultrasound models for diagnosis of early liver trauma.

Journal: Scientific reports
Published Date:

Abstract

The study aimed to develop an AI-assisted ultrasound model for early liver trauma identification, using data from Bama miniature pigs and patients in Beijing, China. A deep learning model was created and fine-tuned with animal and clinical data, achieving high accuracy metrics. In internal tests, the model outperformed both Junior and Senior sonographers. External tests showed the model's effectiveness, with a Dice Similarity Coefficient of 0.74, True Positive Rate of 0.80, Positive Predictive Value of 0.74, and 95% Hausdorff distance of 14.84. The model's performance was comparable to Junior sonographers and slightly lower than Senior sonographers. This AI model shows promise for liver injury detection, offering a valuable tool with diagnostic capabilities similar to those of less experienced human operators.

Authors

  • Qing Song
    School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore. Electronic address: eqsong@ntu.edu.sg.
  • Xuelei He
  • Yanjie Wang
    Zhejiang Key Laboratory of Blood-Stasis-Toxin Syndrome, Zhejiang Chinese Medical University, Hangzhou, 310000, China.
  • Hanjing Gao
    Department of Ultrasound, Second Medical Center, General Hospital of Chinese PLA, Beijing, 100700, China.
  • Li Tan
    Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong, China.
  • Jun Ma
    State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China.
  • Linli Kang
    Department of Ultrasound, First Medical Center of General Hospital of Chinese PLA, Beijing, 100853, China.
  • Peng Han
    Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, Qingdao, China.
  • Yukun Luo
    Department of Ultrasound, First Medical Center of General Hospital of Chinese PLA, Beijing, 100853, China. lyk301@163.com.
  • Kun Wang
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.